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Which are the main applications of edge preserving filters? 


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Edge-preserving filters find applications in various fields such as image processing, computer vision, and graphics. The main applications of edge-preserving filters include image smoothing, denoising, enhancement, structure-preserving texture removal, mutual-structure extraction, HDR tone mapping, stereo matching, optical flow, joint depth map upsampling, edges detection, image abstraction, texture editing . Additionally, these filters are utilized in image matting/feathering, dehazing, detail enhancement, HDR compression, joint upsampling, and more in computer vision and computer graphics applications . Furthermore, edge-preserving filters play a crucial role in diverse applications like learning filter parameters from data, image segmentation tasks, and incorporating bilateral filters in CNNs for high-dimensional sparse data processing . The versatility and effectiveness of edge-preserving filters make them essential tools in various image processing and computer vision tasks.

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Papers (5)Insight
Main applications of edge-preserving filters include image smoothing for preserving salient edges and structure-preserving smoothing for preserving salient structures, as discussed in the paper.
Main applications of edge-preserving filters include edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, and joint upsampling in computer vision and graphics.
Proceedings ArticleDOI
Hui Yin, Yuanhao Gong, Guoping Qiu 
15 Jun 2019
88 Citations
Edge preserving filters are crucial in image smoothing, denoising, enhancement, structure-preserving texture-removing, mutual-structure extraction, HDR tone mapping, colorization, and more in computer vision applications.
Edge-preserving filters are used in image filtering, dense CRFs for image segmentation, and in bilateral neural networks for high-dimensional sparse data processing, as highlighted in the paper.
Main applications of edge-preserving filters include stereo matching, optical flow, joint depth map upsampling, edge-preserving smoothing, edges detection, image abstraction, and texture editing as demonstrated in the paper.

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